Book Image

Python Web Scraping - Second Edition

By : Katharine Jarmul
Book Image

Python Web Scraping - Second Edition

By: Katharine Jarmul

Overview of this book

The Internet contains the most useful set of data ever assembled, most of which is publicly accessible for free. However, this data is not easily usable. It is embedded within the structure and style of websites and needs to be carefully extracted. Web scraping is becoming increasingly useful as a means to gather and make sense of the wealth of information available online. This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you'll see how to extract data from static web pages. You'll learn to use caching with databases and files to save time and manage the load on servers. After covering the basics, you'll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers. You'll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You'll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You'll find out how to automate these actions with Python packages such as mechanize. You'll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites. By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics.
Table of Contents (10 chapters)

Solving complex CAPTCHAs

The CAPTCHA system tested so far was relatively straightforward to solve -- the black font color meant that the text could easily be distinguished from the background, and additionally, the text was level and did not need to be rotated for Tesseract to interpret it accurately. Often, you will find websites using simple custom CAPTCHA systems similar to this, and in these cases, an OCR solution is practical. However, if a website uses a more complex system, such as Google's reCAPTCHA, OCR will take a lot more effort and may become impractical.

In these examples, the text is placed at different angles and with different fonts and colors, so plenty more work needs to be done to clean and preprocess the image before OCR is accurate. These advanced CAPTCHAs can sometimes even be difficult for people to interpret, making it that much more difficult to do so with a simple script.

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